Predictions of pavement condition states on segments of a transportation infrastructure of roadways such as a highways and bridges with satisfactory lead times is a notoriously difficult undertaking. For example, frost development in a transportation infrastructure setting is very hard to forecast due to large error magnitudes within the field of meteorology, whereas frost forecasting requires very accurate data regarding dew point and pavement temperature, which are further dependent upon the material composition of the underlying pavement and substrates of a road, bridge, or other segment feature.
Use of meteorological data to generate pavement and/or bridge deck condition predictions is also problematic, as there are many influencing factors that are highly variable. Some of these factors are the albedo, heat capacity, conductance, texture, and emissivity of the pavement and its substrates, the solar and long wave radiation received at the top surface of the pavement, shading effects by surrounding trees and terrain, the atmospheric temperature, humidity, wind speed, and the various forms of precipitation, as well as the profound effects of winter maintenance and treatment activities, and additionally, characteristics of traffic flow, patterns, and usage. There is no currently-available system or method that considers all of these factors and accounts for their variances to produce a comprehensive model of pavement condition behavior.
Further, there is no existing system or method that incorporates all of traffic, weather, and known road conditions, either real-time or forecasted, to augment the simulation of a pavement's behavior so as to generate a more realistic representation of what current conditions look like and what future conditions will be. There is likewise no existing system or method for generating sophisticated output content for use by motorists, for communication to vehicles for automatic setting adjustments, for private and public entities, or for media consumption in response to such a pavement and road condition model, such as for example visualized representations in the form of cross-sectional time-series animations of pavement conditions.